Validity across four common street-crossing distraction indicators to predict pedestrian safety.

BACKGROUND: Multiple distraction indicators have been applied to measure street-crossing distraction but their validities in predicting pedestrian safety are poorly understood. METHODS: Based on a video-based observational study, we compared the validity of four commonly used distraction indicators (total duration of distraction while crossing a street, proportion of distracted time over total street-crossing time, duration of the longest distraction time, and total number of distractions) in predicting three pedestrian safety outcomes (near-crash incidence, frequency of looking left and right, and speed crossing the street) across three types of distraction (mobile phone use, talking to other pedestrians, eating/drinking/smoking). Change in Harrell's C statistic was calculated to assess the validity of each distraction indicator based on multivariable regression models including only covariates and including both covariates and the distraction indicator. RESULTS: Heterogeneous capacities in predicting the three safety outcomes across the four distraction indicators were observed: 1) duration of the longest distraction time was most predictive for the occurrence of near-crashes and looks left and right among pedestrians with all three types of distraction combined and talking with other pedestrians (Harrell's C statistic changes ranged from 0.0310 to 0.0335, P < 0.05), and for the occurrence of near-crashes for pedestrians involving mobile phone use (Harrell's C statistic change: 0.0053); 2) total duration of distraction was most predictive for speed crossing the street among pedestrians with the combination and each of the three types of distraction (Harrell's C statistic changes ranged from 0.0037 to 0.0111, P < 0.05), frequency of looking left and right among pedestrians distracted by mobile phone use (Harrell's C statistic change: 0.0115), and the occurrence of near-crash among pedestrians eating, drinking, or smoking (Harrell's C statistic change: 0.0119); and 3) the total number of distractions was the most predictive indicator of frequency of looking left and right among pedestrians eating, drinking, or smoking (Harrell's C statistic change: 0.0013). Sensitivity analyses showed the results were robust to change in grouping criteria of the four distraction indicators. CONCLUSIONS: Future research should consider the pedestrian safety outcomes and type of distractions to select the best distraction indicator.

[Epidemiological characteristics and spatial-temporal clustering analysis on pulmonary tuberculosis in Changsha from 2013 to 2016].

OBJECTIVE: To analyze the epidemic characteristics and to explore the spatial-temporal clusters of pulmonary tuberculosis (PTB) in Changsha from 2013 to 2016.
 Methods: Descriptive analysis and space-time permutation scan statistic were used to analyze the reported PTB cases in Changsha from 2013 to 2016.
 Results: Between 2013 and 2016, a total of 17 721 PTB cases were reported in Changsha, with annual reported incidence rate at 60.87 per 100 000 population. Males and individuals aged 15 to <60 years accounted for higher proportion of PTB cases compared to females and other age groups. The number of reported PTB cases reached peak from March to May in each year. The space-time permutation scan statistic identified one most likely cluster and two secondary clusters of PTB cases. The most likely cluster covered most areas of Liuyang City and the North-east part of Changsha County from October 1, 2013 to February 28, 2014. The first cluster occupied 12 towns (streets) in Kaifu District and Changsha County in December 2016. The second cluster included four towns (streets) in Yuhua District and Tianxin District from March 1 to September 30, in 2013.
 Conclusion: Between 2013 and 2016, significant space-time clusters of PTB cases were identified in Changsha. These findings could provide a guide for development of regional intervention strategies for PTB control.